Application of Trained Artificial Intelligence Processes to Encrypted Data Within a Distributed Computing Environment

3. The apparatus of claim 1, wherein the artificial intelligence process comprises a convolutional neural network.

4. The apparatus of claim 1, wherein the encrypted output data is representative of the predicted credit score of the customer during at least one of a current temporal interval or a future temporal interval.

6. The apparatus of claim 5, wherein the executed application program causes the device to present, within the digital interface, an additional graphical representation of a difference between the predicted credit score and the prior credit score, the graphical representation having a visual characteristic that reflects a magnitude of the difference.

9. The apparatus of claim 7, wherein the executed application program causes the device to modify a portion of the digital interface based on the notification, the modified portion of the digital interface being indicative of a difference between the predicted credit score and the prior credit score, the modified portion of the digital interface having a visual characteristic that reflects a magnitude of the difference.

12. The apparatus of claim 11, wherein the third-party computing system is further configured to generate an encrypted training data based on portions of the encrypted customer and reporting data, and perform operations that adaptively train the artificial intelligence process using the encrypted training data.

13. The apparatus of claim 1, wherein the at least one processor is further configured to execute the instructions to transmit the notification to the device via the communications when the predicted credit score fails to exceed the threshold score, and when the predicted credit score exceeds an additional threshold score.

16. The apparatus of claim 15, wherein the trained artificial intelligence process comprises a trained convolutional neural network.